An automation network provides packet-based communication between the host and a client, wherein the client determines output values from the host in the event of errors in the communication between the host and the client, where the determination of output data can be performed in a separate local processing module in accordance with a less complex method than on the host, such that it becomes possible to perform complex open-loop and closed-loop control tasks on the host even in the case of mobile clients or other clients that are difficult to wire.
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2. The method as claimed in claim 1, wherein the replacement output value packets are generated by the at least one client device with the aid of a local processing module assigned to the at least one client device.
This invention relates to a system for generating replacement output value packets in a distributed computing environment. The problem addressed is the need for efficient and reliable data processing in systems where multiple client devices interact with a central server, particularly when handling output values that may require modification or replacement. The method involves at least one client device generating replacement output value packets to replace original output values. These replacement packets are created with the aid of a local processing module assigned to the client device. The local processing module ensures that the replacement process is handled efficiently at the client level, reducing the computational load on the central server and improving overall system performance. The replacement output value packets are then transmitted to the server, which processes them as needed. The system may also include a server that receives and processes the replacement output value packets. The server may further validate or modify the packets before integrating them into the system's workflow. The local processing module on the client device may perform tasks such as data transformation, error correction, or value substitution to generate the replacement packets. This decentralized approach enhances scalability and fault tolerance in the system. The invention is particularly useful in applications where real-time data processing is critical, such as financial transactions, sensor data aggregation, or distributed computing tasks. By offloading processing tasks to client devices, the system reduces latency and improves responsiveness. The method ensures that replacement values are generated accurately and efficiently, maintaining data integrity and system reliabil
3. The method as claimed in claim 2, wherein the host device transmits parameters for the local processing module to the at least one client device.
A system and method for distributed processing involves a host device coordinating computational tasks with one or more client devices. The host device manages task allocation, ensuring efficient utilization of client device resources. The system addresses the challenge of optimizing computational workload distribution across heterogeneous devices, improving overall processing efficiency and reducing latency. The host device dynamically assigns tasks to client devices based on their capabilities and current load, ensuring balanced resource utilization. In addition to task allocation, the host device transmits specific parameters to the local processing modules of the client devices. These parameters configure the client-side processing modules to execute tasks according to predefined criteria, such as computational constraints or performance requirements. The system may also include mechanisms for monitoring task progress and adjusting parameters in real-time to adapt to changing conditions. By centralizing control while enabling flexible client-side processing, the system enhances scalability and adaptability in distributed computing environments. The method ensures that client devices operate within specified constraints while maximizing overall system throughput.
4. The method as claimed in claim 2, wherein the host device transmits the local processing module to the at least one client device.
A system and method for distributed computing involves a host device managing computational tasks across multiple client devices. The host device identifies tasks that can be offloaded to client devices to improve overall system efficiency. The host device determines the capabilities of each client device, such as processing power, memory, and network bandwidth, to assign tasks appropriately. The host device then distributes the tasks to the client devices, which execute them locally and return the results. This approach reduces the computational load on the host device and leverages the combined processing power of the client devices. In one implementation, the host device transmits a local processing module to the client devices. This module enables the client devices to perform the assigned tasks without requiring extensive pre-installed software. The module may include necessary algorithms, data processing routines, or other computational resources needed to execute the tasks. By dynamically distributing this module, the system ensures that client devices can handle tasks even if they lack the required software initially. This enhances flexibility and scalability, allowing the system to adapt to varying workloads and device capabilities. The results from the client devices are then aggregated by the host device for further analysis or output. This distributed processing approach optimizes resource utilization and improves performance in computing environments with multiple interconnected devices.
5. The method as claimed in claim 4, wherein the host device transmits the local processing module to the at least one client device.
A system and method for distributed computing involves a host device managing computational tasks across multiple client devices. The host device identifies tasks that can be offloaded to client devices, such as mobile devices or edge computing nodes, to improve efficiency or reduce latency. The host device determines the capabilities of each client device, including processing power, memory, and network conditions, to assign tasks appropriately. The host device then transmits a local processing module to at least one client device, enabling the client device to execute the assigned task independently. The local processing module includes instructions and data required for the task, allowing the client device to process the task without further intervention from the host. After processing, the client device returns the results to the host device, which aggregates the results and completes the overall computation. This approach optimizes resource utilization by leveraging distributed computing power while minimizing the load on the host device. The system is particularly useful in scenarios where real-time processing or low-latency responses are required, such as in edge computing, IoT applications, or collaborative computing environments.
6. The method as claimed in claim 4, wherein the host device transmitting the local processing module to the at least one client device via an alternative communication connection.
A system and method for distributed computing involves a host device that offloads processing tasks to one or more client devices. The host device includes a local processing module that performs computations and communicates with the client devices. The client devices execute tasks assigned by the host device, reducing the computational load on the host. The host device monitors the performance of the client devices and dynamically adjusts task distribution based on available resources. To ensure reliable communication, the host device establishes a primary communication connection with the client devices. If the primary connection fails or becomes unstable, the host device automatically switches to an alternative communication connection to maintain uninterrupted data transfer. This alternative connection may include different network protocols, wireless channels, or backup communication pathways. The system optimizes processing efficiency by leveraging distributed computing while ensuring robust connectivity through redundant communication channels. The method improves scalability and fault tolerance in distributed computing environments by dynamically managing task allocation and communication pathways.
7. The method as claimed in claim 4, wherein the host device creates the local processing module based on a base operating point predetermined by the host device.
8. The method as claimed in claim 1, wherein the host device determines the at least one predetermined base operating point at predetermined times.
11. The method as claimed in claim 10, wherein the host device generates the output value packets for determining the manipulated variable by processing the input value packets created by the at least one client device from the measurement variable.
12. The method as claimed in claim 1, wherein the at least one client device generates the replacement output value packets until a temporal or local limitation in an event that at least one packet is not received within a predefined period on the packet-based communication connection.
This invention relates to packet-based communication systems, specifically addressing reliability issues in real-time data transmission where packet loss or delays can disrupt communication. The method involves a client device generating replacement output value packets when expected packets are not received within a predefined timeframe, ensuring continuous data flow despite network disruptions. The replacement packets are generated based on temporal or local limitations, such as time constraints or device-specific conditions, to maintain synchronization and prevent gaps in the transmitted data stream. This approach is particularly useful in applications like real-time audio, video, or sensor data transmission where uninterrupted data flow is critical. The method dynamically compensates for missing packets by generating substitute data, allowing the communication system to operate smoothly even under adverse network conditions. The solution enhances reliability without requiring retransmission requests, reducing latency and improving overall system performance. The invention is applicable in various fields, including telecommunications, IoT, and multimedia streaming, where maintaining data integrity and continuity is essential.
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February 3, 2021
November 22, 2022
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